No abstract

Coronavirus disease 2019 (COVID-19) is a major threat worldwide due to its fast spreading. As yet, there are no established drugs or vaccines available. Speeding up drug discovery is urgently required. We applied a workflow of combined in silico methods (virtual drug screening, molecular docking and supervised machine learning algorithms) to identify novel drug candidates against COVID-19. We constructed chemical libraries consisting of FDA-approved drugs for drug repositioning and of natural compound datasets from literature mining and the ZINC database to select compounds interacting with SARS-CoV-2 target proteins (spike protein, nucleocapsid protein, and 2'-o-ribose methyltransferase). Supported by the supercomputer MOGON II, candidate compounds were predicted as presumable SARS-CoV-2 inhibitors. Interestingly, several approved drugs against hepatitis C virus (HCV), another enveloped (-) ssRNA virus (paritaprevir, simeprevir, grazoprevir, and velpatasvir) as well as drugs against transmissible diseases, against cancer, or other diseases were identified as candidates against SARS-CoV-2. This result is supported by reports that anti-HCV compounds are also active against Middle East Respiratory Virus Syndrome (MERS) coronavirus. The candidate compounds identified by us may help to speed up the drug development against SARS-CoV-2.

Gene expression profiling was performed on the human neuroglial cell line T98G after treatment with adaptogen ADAPT-232 and its constituents – extracts of Eleutherococcus senticosus root, Schisandra chinensis berry, and Rhodiola rosea root as well as several constituents individually, namely, eleutheroside E, schizandrin B, salidroside, triandrin, and tyrosol. A common feature for all tested adaptogens was their effect on G-protein-coupled receptor signaling pathways, i.e., cAMP, phospholipase C (PLC), and phosphatidylinositol signal transduction pathways. Adaptogens may reduce the cAMP level in brain cells by down-regulation of adenylate cyclase gene ADC2Y and up-regulation of phosphodiesterase gene PDE4D that is essential for energy homeostasis as well as for switching from catabolic to anabolic states and vice versa. Down-regulation of cAMP by adaptogens may decrease cAMP-dependent protein kinase A activity in various cells resulting in inhibition stress-induced catabolic transformations and saving of ATP for many ATP-dependant metabolic transformations. All tested adaptogens up-regulated the PLCB1 gene, which encodes phosphoinositide-specific PLC and phosphatidylinositol 3-kinases (PI3Ks), key players for the regulation of NF-κB-mediated defense responses. Other common targets of adaptogens included genes encoding ERα estrogen receptor (2.9–22.6 fold down-regulation), cholesterol ester transfer protein (5.1–10.6 fold down-regulation), heat shock protein Hsp70 (3.0–45.0 fold up-regulation), serpin peptidase inhibitor (neuroserpin), and 5-HT3 receptor of serotonin (2.2–6.6 fold down-regulation). These findings can be reconciled with the observed beneficial effects of adaptogens in behavioral, mental, and aging-associated disorders. Combining two or more active substances in one mixture significantly changes deregulated genes profiles: synergetic interactions result in activation of genes that none of the individual substances affected, while antagonistic interactions result in suppression some genes activated by individual substances. These interactions can have an influence on transcriptional control of metabolic regulation both on the cellular level and the level of the whole organism. Merging of deregulated genes array profiles and intracellular networks is specific to the new substance with unique pharmacological characteristics. Presumably, this phenomenon could be used to eliminate undesirable effects (e.g., toxic effects) and increase the selectivity of pharmacological intervention.

No abstract

Coronavirus disease 2019 (COVID-19) is a major threat worldwide due to its fast spreading. As yet, there are no established drugs or vaccines available. Speeding up drug discovery is urgently required. We applied a workflow of combined in silico methods (virtual drug screening, molecular docking and supervised machine learning algorithms) to identify novel drug candidates against COVID-19. We constructed chemical libraries consisting of FDA-approved drugs for drug repositioning and of natural compound datasets from literature mining and the ZINC database to select compounds interacting with SARS-CoV-2 target proteins (spike protein, nucleocapsid protein, and 2’-o-ribose methyltransferase). Supported by the supercomputer MOGON, candidate compounds were predicted as presumable SARS-CoV-2 inhibitors. Interestingly, several approved drugs against hepatitis C virus (HCV), another enveloped (-) ssRNA virus (paritaprevir, simeprevir and velpatasvir) as well as drugs against transmissible diseases, against cancer, or other diseases were identified as candidates against SARS-CoV-2. This result is supported by reports that anti-HCV compounds are also active against Middle East Respiratory Virus Syndrome (MERS) coronavirus. The candidate compounds identified by us may help to speed up the drug development against SARS-CoV-2.

No abstract

No abstract

No abstract